The aim of this study was twofold: evaluate different approaches to deriving normalised digital
surface models (nDSM), and develop a robust and transferable methodology for mapping
informal dwellings. In the first component, three approaches to extract nDSMs were
investigated: (i) light detection and ranging (LiDAR) data, (ii) high resolution aerial photographs
in a process of image matching, and (iii) a series of aerial images captured using a hand-held
camera using structure from motion (SfM) techniques. SfM is a relatively new technique that has
not been widely used for nDSM extraction. This study represented a first attempt at evaluating
the three approaches, particularly for mapping informal dwellings. The accuracy of the
respective nDSMs was evaluated using vertical profiles, area-based, as well as positional-based
accuracy assessment metrics. This provided a clear indication of the robustness of each of the
models. Results showed that an nDSM can be successfully extracted in an informal settlement
for informal dwelling mapping. Overall LiDAR achieved the highest accuracy in all three
accuracy assessments, showing its ability to handle the undefined and complex morphology of
informal settlements. To further test the robustness of the nDSMs, each model was applied to an
independent test site with varying dwelling density and achieved improved accuracies.
In the second component, the utility of high resolution WorldView-2 imagery and object-based
image analysis (OBIA) techniques to develop a robust and transferable methodology for
mapping individual informal dwellings in the City of Cape Town was tested. A systematic
approach was used to objectively identify segmentation and classification parameters. The
supervised segmentation parameter tuner (SPT) tool was used to derive optimal segmentation
parameters, and was evaluated using an area-based accuracy assessment which resulted in high
compactness (> 86%) and correctness (>88%). To reduce data dimensionality and optimize the
classification process, the RF algorithm reduced the original WV-2 feature set (n=40) and aerial
imagery (n=60) feature sets by 23% and 53%, whereas the CART algorithm reduced the same
feature set by 95% and 91% respectively. For classification, a supervised approach was adopted
using the random forest (RF) algorithm, and a rule-based classification using a rule set in
eCognition software. Although different feature subsets were selected by the RF and CART
algorithm for the WV-2 and aerial imagery, similar classification accuracies were achieved in all
the test sites